Spectral calibration curves

Spectral calibration curves for a t)247 quartz prism monochromator with either an EMI 6256 or 9558 photomultiplier are shown in Figure 5. Most of the spectra to be presented later have not been corrected but... 

Accuracy When spectral and chemical interferences are minimized, accuracies of 0.5-5% are routinely possible. With nonlinear calibration curves, higher accuracy is obtained by using a pair of standards whose absorbances closely bracket the sample s absorbance and assuming that the change in absorbance is linear over the limited concentration range. Determinate errors for electrothermal atomization are frequently greater than that obtained with flame atomization due to more serious matrix interferences. 

Table 8.7). Thus, intensity and concentration are directly proportional. However, the intensity of a spectral line is very sensitive to changes in flame temperature because such changes can have a pronounced effect on the small proportion of atoms occupying excited levels compared to those in the ground state (p. 274). Quantitative measurements are made by reference to a previously prepared calibration curve or by the method of standard addition. In either case, the conditions for measurement must be carefully optimized with reference to the choice of emission line, flame temperature, concentration range of samples and linearity of response. Relative precision is of the order of 1-4%. Flame emission measurements are susceptible to interferences from numerous sources which may enhance or depress line intensities. 

As many physicochemical characteristics as possible of reactants, possible intermediates and products should be considered. These might include spectral features (IR, UV-vis, NMR), ion conductivities (if ions participate in the reaction), optical activity, etc. If such data are not available in the literature, they should be investigated early on as they may lead to a monitoring procedure for the kinetic study. Although physicochemical properties which are directly proportional to concentration are most convenient, others such as pH or electrode potentials may be used as their relationship to concentration is well understood. When the relationship between an observed property or measured signal and concentration of a component in the reaction mixture is not theoretically derived, e.g. GLC signals from analysed samples of the reaction mixture, calibration curves may be used. These are constructed by analysis of standard solutions of a reaction component (see Chapter 2). 

For analysis of minor components down to trace amounts or when continuous calibration curves are necessary, it is possible in certain cases to use specially adapted, mathematically simplified and hence speeded-up algorithms. Anyhow, here, even more than with classification tasks, the performance depends very much on the selection of a suitable algorithm and the careful adaptation towards the specific problem. In any case, such algorithms frequently are not overly stable against unforeseen spectral interferences. 

Even though linearity tests are satisfactory (correlation coefficient r is above 0.995) for characterizing the spectro(photo)meter performance, in most of the cases, the curves show that the increase of the spectral bandwidth causes an apparent decrease in absorbance from the true absorbance. The accuracy of the spectro(pho-to)metric results is related both to the performance of the instrument and to the uncertainty due to the linear calibration curve (of the instrument) and, therefore, this uncertainty component must be evaluated. 

Figure 15.8 Examples of spectral integration and normalization. Spectra shown were obtained with nitroxide label 14 (Fig. 15.3C). Acquisition parameters are listed in Table 15.1, except that number of scans = 4 and number of points = 1024. (A) Spectrum of an aqueous sample of a 23-nt RNA, together with its 1st and 2nd integrals. (B) Spectral comparison between a 23-nt RNA (40 gM, dotted line) and a 49-nt RNA (30 jiM, sobd Une). Comparison of the normalized spectra is not skewed by the different amount of labeled RNAs used in the measurement, and reports different nitroxide behavior due primarily to the difference in RNA size. (C) An example of spin counting. The calibration curve was generated by linear fitting (solid Une) of data points (sobd square) obtained using tempol solutions of various concentrations. Using this calibration curve, the sample measured in (A) was found to contain 37.5 gM of spins ( sample = 2.5). Based on an RNA concentration of 40 jiM, the nitroxide labeling efficiency was determined to be 93.6%.

In spectrophotometric analyzers, interference filters are selected for desired wavelengths, as determined from the spectral relationship curves. Photodetectors are least sensitive in the blue end of the spectrum. This can be dealt with by using prefilters or narrow spectral ranges, which are calibrated for more sensitivity. Improvements in spectrophotometers include a flashed xenon light source with dual-beam measurement. Dual-beam machines measure the spectrum of both the light source and the reflected light for each measurement. 

Physical and chemical effects can be combined for identification as sample matrix effects. Matrix effects alter the slope of calibration curves, while spectral interferences cause parallel shifts in the calibration curve. The water-methanol data set contains matrix effects stemming from chemical interferences. As already noted in Section 5.2, using the univariate calibration defined in Equation 5.4 requires an interference-free wavelength. Going to multivariate models can correct for spectral interferences and some matrix effects. The standard addition method described in Section 5.7 can be used in some cases to correct for matrix effects. Severe matrix effects can cause nonlinear responses requiring a nonlinear modeling method. 

The ICP-AES and ICP-MS techniques may also suffer from matrix effects, such as spray chamber effects caused by the different viscosity of the samples and the calibration standards. The careful choice of internal standards can reduce this problem. The effects caused by high amounts of easily ionized elements may be solved by internal standardization or by the use of matrix-matched calibration curves. An additional specific problem with ICP-AES is the risk of spectral overlaps. 

A major detraction for LS AAS has always been the relatively short linear region of the calibration curves, typically not more than two orders of magnitude in concentration. The limits of the linear working range arise from stray radiation and the finite width of the emission lines of the radiation source, which is not monochromatic and just three to five times narrower than the absorption profile. With HR-CS AAS, there is no theoretical limit to the calibration range, only the practical limits imposed by the size of the array detector, the increasing possibility of spectral interferences, and the ability to clean the atomizer after extremely high analyte concentrations have been introduced. 

Even the narrowest spectral bandpass obtainable with most spectrometers will not isolate the most sensitive 248.33 nm iron line from the nonabsorbing iron line at 248.42 nm and other nearby lines. Iron calibration curves, therefore, will usually be non-linear. The 302.05/302.06 nm iron line has been reported to have a better signal/noise ratio [194]. 

This method may be applied to solid, liquid, or gaseous samples. Considering the fact that the difference in wavenumbers between the Stokes and the anti-Stokes signals is frequently large, the spectral sensitivity of the detector should be taken into account. A calibration curve may be obtained, as proposed by D Orazio and Schrader (1974). A typical example is shown in Fig. 6.8-16 see also Sec. 2.4 and Fig. 2.4-2. 

Figure 6.8-16 Calibration curve for the spectral sensitivity of a detector tube.

The concentrations of the CH and CN radicals were determined to have maximum values of (1.3 0.5) x 10 cm and (3.9 1.5) x 10 ° cm respectively. CH and CN-LIF measurements were carried out under linear excitation conditions. This was verified for the strongest optical transitions of both radicals by plotting the LIF signal intensity versus laser energy, as shown in Part (a) of Fig. 8. For each data point the laser was scanned over the complete spectral line to account for possible background signals. N2 Rayleigh calibration curves are shown in Part (b) of Fig. 8. 

Inductively coupled plasma-atomic emission spectrometry was investigated for simultaneous multielement determinations in human urine. Emission intensities of constant, added amounts of internal reference elements were used to compensate for variations in nebulization efficiency. Spectral background and stray-light contributions were measured, and their effects were eliminated with a minicomputer-con-trolled background correction scheme. Analyte concentrations were determined by the method of additions and by reference to analytical calibration curves. Internal reference and background correction techniques provided significant improvements in accuracy. However, with the simple sample preparation procedure that was used, lack of sufficient detecting power prevented quantitative determination of normal levels of many trace elements in urine. 

Our group proposed calibration curves for the four bases, again under rigorously similar pyrolytic conditions. The curves were obtained from a spectral compilation of over 60 different DNAs (Fig. 19). Although in general the application of our curves gives better results, it remains that such a statistical study is of very limited use. The four equations for the calculation of base abundances from the ion intensities are /I =0.61 —0.35,... 

If the system has a spectral detector, spectral evaluation such as peak purity and compound identity should be part of the test. Additional tests should be developed if there are other software functions used in routine analysis, but they are not part of the sample or standard analysis test. If the system is used over a wide concentration range with multiple calibration points, the tests should be run over many concentrations to verify correct function of the calibration curve. 

---